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An Evaluation of Geo-located Twitter Data for Measuring Human Migration
Junjun Yin1, Yizhao Gao2,3, Guangqing Chi1,4
1Social Science Research Institute and Population Research Institute, The Pennsylvania State University, University Park, PA, 16802, USA.
International Journal of Geographical Information Science : IJGIS
|January 16, 2023
Summary
Geo-located Twitter data can measure human migration patterns. This study found Twitter data under-represents large population areas but over-represents tourist-adjacent regions compared to IRS data.
Area of Science:
- Computational Social Science
- Geospatial Analysis
- Human Mobility Studies
Background:
- Traditional migration data sources, like IRS data, have limitations in granularity and timeliness.
- Geo-located social media data offers a potential alternative for tracking population movements.
Purpose of the Study:
- To evaluate the spatial patterns of human migration flows derived from geo-located Twitter data.
- To compare Twitter-estimated migration with U.S. Internal Revenue Service (IRS) data.
- To identify and analyze spatial discrepancies in Twitter-based migration estimates.
Main Methods:
- Utilized geo-located Twitter data from 2013-2015 to identify migrating users based on county-of-residence changes.
- Developed a normalized difference representation index to assess spatial over/under-representation of Twitter migration flows against IRS data.
- Applied a multidimensional spatial scan statistic with a Poisson process model to detect significant regional over/under-representation patterns.
Main Results:
- Twitter migration flows showed under-representation in high-population regions.
- Twitter migration flows demonstrated over-representation in metropolitan areas near tourist attractions.
- Significant spatial variations in Twitter migration data accuracy were identified.
Conclusions:
- Geo-located Twitter data serves as a viable statistical proxy for measuring human migration.
- Understanding spatial patterns of Twitter migration data is crucial for accurate analysis and calibration.
- This methodology aids in identifying regions requiring data adjustments for improved migration studies.
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